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Rigorous policy measurement: causal inference challenges and opportunities
Journal article   Peer reviewed

Rigorous policy measurement: causal inference challenges and opportunities

Alina Schnake-Mahl, Ana V. Diez Roux, Usama Bilal, Gabriel L. Schwartz and Scott Burris
American journal of epidemiology, v 194(11)
06 Jan 2025
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://pmc.ncbi.nlm.nih.gov/articles/PMC12782659/View
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Abstract

Life Sciences & Biomedicine Public, Environmental & Occupational Health Science & Technology
Epidemiologists are increasingly asking questions about the effects of policies on health and health disparities, generally using quasi-experimental methods. Researchers have developed a burgeoning body of rigorous methodological work focused on addressing potential inference challenges arising from modeling choices, study design, data availability, and common sources of bias in policy evaluations using observational data. However, epidemiologists have paid less attention to the measurement and operationalization of policy exposures. The field of legal epidemiology offers rigorous, formalized methods to address challenges in measuring policy, yet disciplinary divides have impeded the communication of these approaches from lawyers to epidemiologists. In this article, we use terminology familiar to epidemiologists to describe the field of legal epidemiology and how challenges in measuring policy exposures can compromise causal inference, with a particular focus on addressing information bias and consistency assumptions. Laws and regulations can address or enforce structural inequities, and understanding challenges to their characterization and measurement can enhance epidemiologic research on their health and health equity effects.This article is part of a Special Collection on Methods in Social Epidemiology.

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Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
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